Cumincad : CUMINCAD References : Search Results

CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

PDF papers
References

Hits 41 to 60 of 11636

Reformat results as: short short into frame detailed detailed into frame

Details	Citation	Select
	Jang, J.S.R. and Sun, C.T. (1993) Functional Equivalence Between Radial Basis Function Networks and Fuzzy Inference Systems , IEEE Trans. Neural Networks, Vol.4, No 1

	Janssen, P., Frazer, J. and Tang, M.X. (2001) Evolutionary design systems and generative processes , P.J. Bentley and D.W. Corne (eds), Special Issue on Creative Evolutionary Systems, The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies

	Ji, S., Pan, S., Cambria, E., Marttinen, P., & Yu, P. S. (2022) A Survey on Knowledge Graphs: Representation, Acquisition, and Applications , IEEE Transactions on Neural Networks and Learning Systems, 33(2), 494-514. https://doi.org/10.1109/TNNLS.2021.3070843

	Johanes, M. & Huang, J. (2021) Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey , Proceedings of the 41st Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA), 134-141. https://doi.org/10.52842/conf.acadia.2021.001

	Jürgen, Schmidhuber (2015) Deep learning in neural networks: An overview , Neural networks, 61, pp. 85-117

	Kandel, ER (1991) Nerve cells and behavior , Principles of neural science, 2, pp. 24-25

	Karhunen J. and Joutsensalo J. (1995) Generalizations of principal component analysis, optimization problems, and neural networks , Neural Networks; 8: 549–562

	Karras T, Aittala M, Hellsten J, et al (2020) Training generative adversarial networks with limited data , Advances in Neural Information Processing Systems 2020; 33: 12104–12114.

	Karras, T., Aittala, M., Hellsten, J., Laine, S., Lehtinen, J., and Aila, T. (2020) Training Generative Adversarial Networks with Limited Data , Advances in Neural Information Processing Systems, 33:12104-14. Curran Associates, Inc

	Kasaboc, NK (1996) Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering (Computational Intelligence). , MIT Press, Cambridge

	Kasabov, N.K. (1996) Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering , The MIT Press, Cambridge, Massachusetts

	Kasabov, N.K. (1996) Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering , The MIT Press, Cambridge, Massachusetts

	Kaspar A, Oh T-H, Makatura L, et al. (2019) Neural inverse knitting: from images to manufacturing instructions , Proceedings of 36th international conference on machine learning (ICML), PMLR. Long Beach, CA, 10–15 June 2019

	Kato H, Ushiku Y and Harada T. (2018) Neural 3d mesh renderer. , Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, Institue of Electrical and Electronics Engineering (IEEE), Salt Lake City, Utah, USA, pp.3907–3916.

	Kato, H., Ushiku, Y. and Harada, T. (2018) Neural 3d mesh renderer , Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3907-3916

	Kato, Hiroharu, Yoshitaka Ushiku, and Tatsuya Harada (2018) Neural 3D Mesh Renderer , Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, 18–23 June 2018, 3907–3916. IEEE

	Kazanasmaz T, Günaydin M and Binol S (2009) Artificial neural networks to predict daylight illuminance in office buildings , Build Environ 2009; 44: 1751–1757

	Kazanasmaz, T, Günaydin, M and Binol, S (2009) Artificial neural networks to predict daylight illuminance in office buildings , Building and Environment, 44(8), pp. 1751-1757

	Kazanasmaz, T., Gunaydin, M. & Binol, S., (2009) Artificial Neural Networks to Predict Daylight Illuminance in Office Buildings , Building and Environment, pp. 1751-1757, Available at: https://doi.org/1.116/j.buildenv.28.11.12.

	Kazanasmaz, T., Günaydin, M. & Binol, S. (2009) Artificial neural networks to predict daylight illuminance in office buildings , Building and Environment, 44(8), 1751–1757

For more results click below: